Standardization of rounds on a general paediatric ward: Implementation of a checklist to improve efficiency, quality of rounds, and family satisfaction
Bibliographic record
Abstract
Objective: The purpose of this study was to develop a standardized rounding tool for use on the general paediatric ward and to determine if its use can improve quality of rounds as well as patient and parent satisfaction. Methodology: The study used a pre- and post-intervention prospective observational methodology. The intervention consisted of the implementation of a Checklist Rounding Tool (CRT) entitled the 'Paediatric Inpatient Rounding Checklist (PIRC)' which outlined items deemed essential to discuss during rounds for all patients admitted to the paediatric ward. The PIRC was created by the research team after reviewing the literature and it was peer reviewed by a panel of expert paediatricians. Performance on rounds based on discussion of checklist items as well as patient and parent satisfaction were evaluated by an external observer both pre- and post-PIRC implementation. Results: Four of the five less frequently addressed checklist items were discussed significantly more post-intervention. The Rounds Quality Score was significantly improved after checklist implementation, the pre- and post-intervention scores being 8.24 and 9.61/10, respectively (P-value <0.001). Patient and parent satisfaction were rated higher with the use of the checklist. There was no difference in the duration of rounds between the pre- and post-implementation phases. Conclusion: In summary, utilization of a standardized rounding tool on an inpatient paediatric ward led to improvement in quality of rounds as well as patient and parent satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".